Linguistic Pythagorean Hypersoft Set with Application: Decision Making in Medical Diagnosis and Treatment

This study explores the role of Linguistic Pythagorean Hypersoft set (LPHS) in addressing the inherent uncertainty and vagueness present in medical diagnosis and treatment due to the limitations of natural language. The purpose of the study is to develop a structured decision-making framework using LPHS that can model linguistic imprecision and manage complex, multidimensional medical information more effectively. By categorizing medical conditions and treatment parameters such as disease severity and treatment effectiveness into linguistic terms and assigning them Pythagorean values, the framework supports a more detailed analysis of patient data. The findings suggest that LPHS improves the accuracy and uniformity of medical decisions, “reflecting its potential to standardize diagnostic outcomes." aids in identifying more accurate diagnoses, and supports more tailored and effective treatment strategies. These outcomes have the potential to improve patient care, assist healthcare professionals in managing uncertainty, and contribute to more informed clinical and policy decisions in the medical field.